A SQL statement can be executed in many different ways, such as full table scans, index scans, nested loops, and hash joins. The query optimizer determines the most efficient way to execute a SQL statement after considering many factors related to the objects referenced and the conditions specified in the query. This determination is an important step in the processing of any SQL statement and can greatly affect execution time.


The EXPLAIN PLAN results let you determine whether the optimizer selects a particular execution plan, such as, nested loops join. It also helps you to understand the optimizer decisions, such as why the optimizer chose a nested loops join instead of a hash join, and lets you understand the performance of a query.




You can examine the execution plan chosen by the optimizer for a SQL statement by using the EXPLAIN PLAN button on the worksheet. When the statement is issued, the optimizer chooses an execution plan and then inserts data describing the plan into a database table. SQL Developer looks at the table and displays the tree operations.  Looking at the example, we can see the query is doing a Cartesian product

Similary, when you use autotrace, which, on SQL Developer, is the button beside the explain plan. AND, you can diff two plans to  see what the difference is between two plans, as you can see below.

However, even though we can show you what a plan looks like, it cannot differentiate between well-tuned statements and those that perform poorly.



For example, an EXPLAIN PLAN output that shows that a statement uses an index does not necessarily mean that the statement runs efficiently. Sometimes indexes can be extremely inefficient. In this case, you should examine the following:
  • The columns of the index being used
  • Their selectivity (fraction of table being accessed)
It is best to use EXPLAIN PLAN to determine an access plan, and then later prove that it is the optimal plan through testing. When evaluating a plan, examine the statement's actual resource consumption.

And from the doc!: 

In addition to running the EXPLAIN PLAN command and displaying the plan, you can use the V$SQL_PLAN views to display the execution plan of a SQL statement. After the statement has executed, you can display the plan by querying the V$SQL_PLAN view. V$SQL_PLAN contains the execution plan for every statement stored in the cursor cache. 

The V$SQL_PLAN_STATISTICS view provides the actual execution statistics for every operation in the plan, such as the number of output rows and elapsed time. All statistics, except the number of output rows, are cumulative. For example, the statistics for a join operation also includes the statistics for its two inputs. The statistics in V$SQL_PLAN_STATISTICS are available for cursors that have been compiled with the STATISTICS_LEVEL initialization parameter set toALL.

The V$SQL_PLAN_STATISTICS_ALL view enables side by side comparisons of the estimates that the optimizer provides for the number of rows and elapsed time. This view combines both V$SQL_PLAN and V$SQL_PLAN_STATISTICS information for every cursor.

Anyway, don't take my word for all this, try it on SQLDeveloper today and you can follow Tom Kyte's process for tuning sql statements.





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Try this in bash

git p

You get a file names p or a list of them.  Thats not much good, but there is a great bash completion library which you can source in your .bashrc to give you valid completion targets

$git p
pull
push
$git p


Pull this file and source it in your .bashrc


$wget https://raw.githubusercontent.com/git/git/master/contrib/completion/git-completion.bash
$echo . $PWD/git-completion.bash >> ~/.bashrc


Open up a new terminal window and enjoy.

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